Train your own decision model

Decision models return calibrated probability distributions over predefined answer options, distinguishing them from structured-output LLMs that generate tokens sequentially and expose next-token likelihoods rather than confidence that an answer is correct. They resemble flexible, modern classifier systems because they can receive new questions and answer choices at inference time, making them suitable for routing, scoring, gating, classification, and other bounded decisions where confidence thresholds can reflect the costs of mistakes. Using a Qwen-based prototype trained on the typed-decisions benchmark, the article shows how multiple independent questions can be packed with one shared input into a single transformer pass through a custom attention mask, reducing processing time while preserving outputs. Its experiments find that training against full target probability distributions with cross-entropy, rather than conventional single-label fine-tuning, is the main source of well-calibrated predictions: both a dedicated decision head and a language-model-head readout achieved similar test accuracy of about 0.78 and low calibration error near 0.02, while the standard fine-tuned baseline was substantially overconfident. Evaluation tracked in Comet and performed in Opik also confirmed question isolation and limited sensitivity to irrelevant options, although reversing answer-option order still altered some trained-model predictions, highlighting an unresolved robustness issue.

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